Publication Type:

Conference Paper

Source:

Proceedings of the Third International Symposium on Women in Computing and Informatics, ACM, New York, NY, USA (2015)

ISBN:

9781450333610

URL:

http://doi.acm.org/10.1145/2791405.2791508

Keywords:

Computational intelligence, Machine learning, NAtural language processing, Short Answer Grading

Abstract:

In this work, we are attempting to grade short answer automatically which can be efficient and helpful to both students and teachers. It uses a combination of many semantic and graph alignment features and is implemented in the Microsoft Azure Machine Learning using Two-class Averaged Perceptron, Linear and Isotonic Regression. We also provide first attempt to use graph alignment features at sentence level. We compare the results of two machine learning algorithms like Two-class Averaged Perceptron and Two-class Support Vector Machine in the results of grading short answers. We have devised novel techniques to apply the concept of Random Projection for grading 150 algorithmic answers on a coding question using our own domain specific corpus which gives precise classification of right and wrong answers.

Cite this Research Publication

R. Krithika and Jayasree Narayanan, “Learning to Grade Short Answers Using Machine Learning Techniques”, in Proceedings of the Third International Symposium on Women in Computing and Informatics, New York, NY, USA, 2015.